Opinion: Looming Automation and Undocumented Knowledge

Safer from AI automation and potentially more agile employees.

Research about potential of AI-driven automation

As the automation revolution gains momentum, discussions about the future of work and the potential impact on employment have become increasingly prevalent. Earlier this year OpenAI published a study on the assessment of exposure of different activities to large language models like their GPT, shedding light on the likelihood of certain jobs being automated. Notably, positions involving text-related tasks, such as writers, researchers, translators, and software engineers, were found to have the highest exposure.

There’s an interesting part of the paper that is about exposure dependence on preparation required and on-the-job experience. Compare these tables:

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By comparing the tables provided, it becomes apparent that positions demanding extensive on-the-job training exhibit lower exposure to automation, while those necessitating longer preparation time exhibit higher exposure. This correlation highlights the importance of experience and tacit knowledge in certain job domains.

Undocumented knowledge

While large enterprises and multinational corporations have been at the forefront of embracing automation technologies, small businesses have been comparatively slow to adapt. One significant reason for this delay is the abundance of undocumented and implicit knowledge that exists within these companies.

Undocumented knowledge refers to the tacit information, skills, and expertise that reside in the minds of employees but are not explicitly documented or codified - a secret sauce, an unwritten wisdom. In small or medium size businesses, this knowledge often plays a critical role in day-to-day operations. It encompasses the unwritten rules, unique processes, and deep industry insights that have been accumulated over years of experience. Unlike larger organizations with well-established knowledge management systems, small businesses heavily rely on the collective knowledge of their tight-knit teams.

This undocumented knowledge can pose both a challenge and an advantage when it comes to automation. On the one hand, small businesses may find it difficult to automate certain tasks or processes that heavily rely on implicit knowledge. Automation technologies typically require explicit instructions and data to function efficiently, making it challenging to transfer tacit knowledge into automated systems without proper documentation.

Opportunity?

However, the flip side of this challenge is the opportunity it presents for small businesses to leverage their intimate knowledge to adapt and thrive in an increasingly automated world. Undocumented knowledge often provides small businesses with a competitive edge. It enables them to offer personalized experiences, tailor-made solutions, and a deep understanding of their niche markets. This level of expertise is difficult to replicate through automation alone (and also hardly available to enterprises).

In other words, small companies with AI focus can leverage undocumented knowledge for agile experimentation. By combining their intimate knowledge of industry nuances with automation technologies, they can develop innovative approaches that cater to the unique needs of their customers. This agility allows them to adapt quickly to changing market dynamics and capitalize on emerging opportunities.